November 2024 arXiv papers — page 104

Daniele Ballinari, Alexander Wehrli

We introduce a double/debiased machine learning estimator for the impulse response function in settings where a time series of interest is subjected to multiple discrete treatments, assigned over time, which can have a causal effect on future outcomes. The proposed estimator can rely on fully nonparametric relations between treatment and outcome variables, o

Rina Dechter, Annie Raichev, Alexander Ihler, Jin Tian

This paper focuses on the computational complexity of computing empirical plug-in estimates for causal effect queries. Given a causal graph and observational data, any identifiable causal query can be estimated from an expression over the observed variables, called the estimand. The estimand can then be evaluated by plugging in probabilities computed empiric

M. Barbillon, A. Recio-Blanco, E. Poggio, P. A. Palicio

The distribution of chemical elements in the Galactic disc can reveal fundamental clues on the physical processes that led to the current configuration of our Galaxy. We map chemical azimuthal variations in the disc using individual stellar chemical abundances and discuss their possible connection with the spiral arms and other perturbing mechanisms. Using G

Yuxuan Huang

Large language models has catalyzed the development of personalized dialogue systems, numerous role-playing conversational agents have emerged. While previous research predominantly focused on enhancing the model's capability to follow instructions by designing character profiles, neglecting the psychological factors that drive human conversations. In this p

Juhee Han, Yoojin Choi

The NVMeVirt paper analyzes the implication of storage performance on database engine performance to promote the tunable performance of NVMeVirt. They perform analysis on two very popular database engines, MariaDB and PostgreSQL. The result shows that MariaDB is more efficient when the storage is slow, but PostgreSQL outperforms MariaDB as I/O bandwidth incr

Ruoyu Chen, Weiyi Zhang, Bowen Liu, Xiaolan Chen

The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automatic disease screening but demands substantial data. Collecting and labeling large volumes of ophthalmic images across various modalities encounters several real-world challenges, es

Wei Wang, Zhiquan Lai, Shengwei Li, Weijie Liu

The size of deep learning models has been increasing to enhance model quality. The linear increase in training computation budget with model size means that training an extremely large-scale model is exceedingly time-consuming. Recently, the Mixture of Expert (MoE) has drawn significant attention as it can scale models to extra-large sizes with a stable comp

Chengbo Zhang, Yonglin Li, Zuopeng Xiao

The rise of instant delivery services has reshaped urban spatial structures through the interaction between suppliers and consumers. However, limited research has explored the spatiotemporal dynamics of delivery network structures. This study constructs a time-dependent, multi-layer instant delivery network in the case city of Beijing using a large-scale dat

Xilong Gao, Jia Si, Zhiyong Zhang

Carbon nanotube field-effect transistors (CNT FETs) are regarded as promising candidates for next-generation energy-efficient computing systems. While research has employed the lift-off process to demonstrate the performance of CNT FETs, this method now poses challenges for enhancing individual FET performance and is not suitable for scalable fabrication. In

Yingxu Wang, Nan Yin, Mingyan Xiao, Xinhao Yi

Graph Neural Networks (GNNs) with equivariant properties have achieved significant success in modeling complex dynamic systems and molecular properties. However, their expressiveness ability is limited by: (1) Existing methods often overlook the over-smoothing issue caused by traditional GNN models, as well as the gradient explosion or vanishing problems in

Sydney Anuyah, Victor Bolade, Oluwatosin Agbaakin

This tutorial serves as a comprehensive guide for understanding graph databases, focusing on the fundamentals of graph theory while showcasing practical applications across various fields. It starts by introducing foundational concepts and delves into the structure of graphs through nodes and edges, covering different types such as undirected, directed, weig

Myunsoo Kim, Donghyeon Ki, Seong-Woong Shim, Byung-Jun Lee

As a highly expressive generative model, diffusion models have demonstrated exceptional success across various domains, including image generation, natural language processing, and combinatorial optimization. However, as data distributions grow more complex, training these models to convergence becomes increasingly computationally intensive. While diffusion

Yoojin Choi, Juhee Han, Daehyun Kim

Database benchmarking is an essential method for evaluating and comparing the performance characteristics of a database management system (DBMS). It helps researchers and developers to evaluate the efficacy of their optimizations or newly developed DBMS solutions. Also, companies can benefit by analyzing the performance of DBMS under specific workloads and l

Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Hatem Abou-Zeid

Foundation deep learning (DL) models are general models, designed to learn general, robust and adaptable representations of their target modality, enabling finetuning across a range of downstream tasks. These models are pretrained on large, unlabeled datasets using self-supervised learning (SSL). Foundation models have demonstrated better generalization than

Arit Kumar Bishwas, Mousumi Sen

As quantum computing continues to advance, its ability to compromise widely used cryptographic systems projects a significant challenge to modern cybersecurity. This paper outlines a strategic roadmap for industries to anticipate and mitigate the risks posed by quantum attacks. Our study explores the development of a quantum-resistant cryptographic solutioni

Kenji Fukushima, Yoshimasa Hidaka, Tomoya Uji

We present analytical and numerical calculations for the photon polarization tensor at finite temperature and density in a constant magnetic field. We first discuss the tensor decomposition in the presence of the magnetic field, which breaks rotational symmetry. Then, we analytically perform all the momentum integrations and numerically take the Landau level

Subham Chakraborty, Soumitra SenGupta

Higher curvature f(R) gravity theories are often plagued with Ostragadsky instability. In this work we show that such instability manifests itself in the corresponding dual scalar tensor theory in the scalar sector Lagrangian. We explicitly demonstrate the correspondence between the instabilities that appear in an $f(R)$ model and its corresponding scalar te

Jian Guo, Saizhuo Wang, Yiyan Qi

Multi-stage decision-making is crucial in various real-world artificial intelligence applications, including recommendation systems, autonomous driving, and quantitative investment systems. In quantitative investment, for example, the process typically involves several sequential stages such as factor mining, alpha prediction, portfolio optimization, and som

Seungjun Lee, Hyung-Gu Kang, Minkwan Kim, Gunsu Yun

While hydroxyl radicals ($\cdot$OH) play an important role as potent oxidizing agents in various plasma applications, their high reactivity confines them to a thin layer at the plasma-liquid interface, posing challenges in comprehending the intricate generation and transport processes. Similarly, solvated electrons ($\mathrm{e_{aq}}$), highly reactive reduci

Mohammad Golgol, Anamitra Pal, Vijay Vittal, Christine Fini

The installation of high-capacity fast chargers for electric vehicles (EVs) is posing a significant risk to the distribution grid as the increased demand from widespread residential EV charging could exceed the technical limits of the distribution system. Addressing this issue is critical, given that current infrastructure upgrades to enhance EV hosting capa

Yuanze Hong, Tian zhou, Wanli Wang

We explore the fractional advection-diffusion equation and rare events associated with the ACTRW model. When waiting times have a finite mean but infinite variance, and the displacements follow a narrow distribution, the fractional operator is defined in terms of space rather than time. The far tail of the positional distribution is governed by rare events,

Zhuohan Gu

In this paper, we make a few random explorations that relate directly to the items mentioned in the title. We define transient chains and recurrent chains with "killing", the Green's function, the Laplacian operator, and harmonic functions. We then introduce the loop-erased random walk (LERW) and its relationship with the uniform spanning tree (UST). We fini

Stefan Rettenmayr, Annette Werner

We explore birational geometry of matroids by investigating automorphisms of their coarse Bergman fans. Combinatorial Cremona maps provide such automorphisms of Bergman fans which are not induced by matroid automorphisms. We investigate the structure of matroids allowing combinatorial Cremona maps and prove a realizability criterion in the presence of two di

Byeonggeun Kim, Juntae Lee, Kyuhong Shim, Simyung Chang

Few-Shot Open-Set Recognition (FSOSR) targets a critical real-world challenge, aiming to categorize inputs into known categories, termed closed-set classes, while identifying open-set inputs that fall outside these classes. Although transfer learning where a model is tuned to a given few-shot task has become a prominent paradigm in closed-world, we observe t

Hirokazu Kobayashi, Han Bi, James Jun He, Seishiro Ono

Topological superconductors and Majorana edge modes at their boundaries have been theoretically predicted. However, their experimental observation remains controversial. Recent theoretical studies suggest that chiral Majorana edge modes exhibit distinct spatially-resolved optical conductivity compared to chiral Dirac edge modes. In this work, we investigate

Mike Thelwall, Kayvan Kousha

Purpose: Journal Impact Factors and other citation-based indicators are widely used and abused to help select journals to publish in or to estimate the value of a published article. Nevertheless, citation rates primarily reflect scholarly impact rather than other quality dimensions, including societal impact, originality, and rigour. In contrast, Journal Qua

Ashutosh Dwibedi, Nandita Padhan, Dani Rose J Marattukalam, Arghya Chatterjee

We have attempted to calculate and estimate the spatial diffusion coefficients of D meson through rotating hadron resonance gas, which can be produced in the late stage of peripheral heavy ion collisions. Employing the framework of kinetic theory in relaxation time approximation, and using Einstein's diffusion relation, one can express the spatial diffusion

Abhishek Chakraborty, Taylor L. Patti, Brucek Khailany, Andrew N. Jordan

Effective Hamiltonian calculations for large quantum systems can be both analytically intractable and numerically expensive using standard techniques. In this manuscript, we present numerical techniques inspired by Nonperturbative Analytical Diagonalization (NPAD) and the Magnus expansion for the efficient calculation of effective Hamiltonians. While these t

Zhuolun Li, Evangelos Pournaras

Distributed ledgers rely on consensus protocols to commit messages in an agreed order. In practice, this order is often chosen for profit, which raises fairness concerns. For example, in decentralized finance, nodes exploit transaction order to extract Maximal Extractable Value (MEV). This paper systematizes the mechanisms at the consensus layer that constra

Xiang-hao Chu, Yi-qing Chu, Shou-shan Bao, Hong Zhang

The small Kerr-anti-de Sitter black hole demonstrates instability due to the superradiance of either a massive or massless scalar field. Previous leading-order approximations of the spectrum are inefficient. In particular, the leading-order real part of the eigenfrequency is insensitive to the spin of the black hole. In this work, we improve the analysis by

Vladimir Braverman, Prathamesh Dharangutte, Shreyas Pai, Vihan Shah

We study the dynamic correlation clustering problem with $\textit{adaptive}$ edge label flips. In correlation clustering, we are given a $n$-vertex complete graph whose edges are labeled either $(+)$ or $(-)$, and the goal is to minimize the total number of $(+)$ edges between clusters and the number of $(-)$ edges within clusters. We consider the dynamic se

Yifan Zeng

This paper proposes HistoLens, a multi-layered analysis framework for historical texts based on Large Language Models (LLMs). Using the important Western Han dynasty text "Yantie Lun" as a case study, we demonstrate the framework's potential applications in historical research and education. HistoLens integrates NLP technology (especially LLMs), including na

Bolun Wei

We studies the Newton polygon for the L-function of toric exponential sums attached to a family of two variable generalized hyperkloosterman sum,$f_{t}(x,y)=x^{n}+y+\frac{t}{xy}$ with $t$ the parameter. The explicit Newton polygon is obtained by systematically using Dwork's $\theta_{\infty}$-splitting function with an appropriate choice of basis for cohomolo

Paavo Salminen, David Stenlund

We study the joint moments of occupation times on the legs of a diffusion spider. Specifically, we give a recursive formula for the Laplace transform of the joint moments, which extends earlier results for a one-dimensional diffusion. For a Bessel spider, of which the Brownian spider is a special case, our approach yields an explicit formula for the joint mo

Merihan Alhafnawi, Maca Gomez-Gutierrez, Edmund R. Hunt, Severin Lemaignan

Robot swarms have the potential to help groups of people with social tasks, given their ability to scale to large numbers of robots and users. Developing multi-human-swarm interaction is therefore crucial to support multiple people interacting with the swarm simultaneously - which is an area that is scarcely researched, unlike single-human, single-robot or s

Vincenzo de Martino, Joel Castaño, Fabio Palomba, Xavier Franch

Context: The emergence of Large Language Models (LLMs) has significantly transformed Software Engineering (SE) by providing innovative methods for analyzing software repositories. Objectives: Our objective is to establish a practical framework for future SE researchers needing to enhance the data collection and dataset while conducting software repository mi

Dominik Kowald, Sebastian Scher, Viktoria Pammer-Schindler, Peter Müllner

Artificial intelligence (AI) technologies (re-)shape modern life, driving innovation in a wide range of sectors. However, some AI systems have yielded unexpected or undesirable outcomes or have been used in questionable manners. As a result, there has been a surge in public and academic discussions about aspects that AI systems must fulfill to be considered

Taaha Kazi, Ruiliang Lyu, Sizhe Zhou, Dilek Hakkani-Tur

Traditionally, offline datasets have been used to evaluate task-oriented dialogue (TOD) models. These datasets lack context awareness, making them suboptimal benchmarks for conversational systems. In contrast, user-agents, which are context-aware, can simulate the variability and unpredictability of human conversations, making them better alternatives as eva

Shota Yamazaki, Chenyu Zhang, Takuya Nanri, Akio Shigekane

End-to-end style autonomous driving models have been developed recently. These models lack interpretability of decision-making process from perception to control of the ego vehicle, resulting in anxiety for passengers. To alleviate it, it is effective to build a model which outputs captions describing future behaviors of the ego vehicle and their reason. How

Umberto Guarnotta, Patrick Winkert

In this paper we study quasilinear elliptic Kirchhoff equations driven by a non-homogeneous operator with unbalanced growth and right-hand sides that consist of sub-linear, possibly singular, and super-linear reaction terms. Under very general assumptions we prove the existence of at least two solutions for such problems by using the fibering method along wi

Taewook Kim, Dhruv Agarwal, Jordan Ackerman, Manaswi Saha

Digital media platforms (e.g., science blogs) offer opportunities to communicate scientific content to general audiences at scale. However, these audiences vary in their scientific expertise, literacy levels, and personal backgrounds, making effective science communication challenging. To address this challenge, we designed TranSlider, an AI-powered tool tha

Xiaofeng Zhang, Yihao Quan, Chaochen Gu, Chen Shen

The hallucination problem in multimodal large language models (MLLMs) remains a common issue. Although image tokens occupy a majority of the input sequence of MLLMs, there is limited research to explore the relationship between image tokens and hallucinations. In this paper, we analyze the distribution of attention scores for image tokens across each layer a

Takumi Muto

Coexistent phase of kaon condensates and hyperons [($Y$+$K$) phase] in beta equilibrium with electrons and muons is investigated as a possible form of dense hadronic phase with multi-strangeness. The effective chiral Lagrangian for kaon-baryon and kaon-kaon interactions is utilized within chiral symmetry approach in combination with the interaction model bet

Shaocong Lu, Ryosuke Akashi, Mitsuaki Kawamura, Shinji Tsuneyuki

The superconducting properties of uniformly hole-doped perovskite hydride KMgH$_3$ with varying doping concentration and lattice parameter corresponding to different pressures were investigated from first principles. The superconducting transition temperature ($T_{\mathrm{c}}$) was predicted from the density functional theory for superconductors (SCDFT), whe

Huancheng Chen, Jingtao Li, Weiming Zhuang, Haris Vikalo

Recently, many text-to-image diffusion models have excelled at generating high-resolution images from text but struggle with precise control over spatial composition and object counting. To address these challenges, prior works have developed layout-to-image (L2I) approaches that incorporate layout instructions into text-to-image models. However, existing L2

Pouria Sanjari, Firooz Aflatouni

Metasurfaces can manipulate the amplitude and phase of electromagnetic waves, offering applications ranging from antenna design and cloaking to imaging and communication. Additionally, temporal, and non-linear metasurfaces have the potential to adjust the frequency of impinging waves, driving advancements in frequency conversion, sensing, and quantum systems

Zejun Sun, Guang-Hui Zheng

We develop a far-field geometric optics approximation sampling (GOAS) framework for constructing direct samplers from target measures. The method exploits the connection between the far-field reflector problem and optimal transport with logarithmic cost, leading to a natural primal--dual transport structure. The associated dual reflector provides a reciproca

Jiachang Ye

Let $\lambda_{i}(G)$ be the $i$-th largest Laplacian eigenvalues of graph $G$, where $1\le i\le |V(G)|$. Liu, Yuan, You and Chen [Discrete Math., 341 (2018) 2969--2976] raised the problem for ``Which cospectral graphs have same degree sequences". In this paper, let $W_3$ and $W_5$ be the two graphs as shown in Fig. 2 and let $G$ be a connected graph with $n\

ATLAS Collaboration

A measurement of the $B^0$ meson lifetime using $B^0 \to J/\psi K^{*0}$ decays in data from 13 TeV proton-proton collisions with an integrated luminosity of 140 fb$^{-1}$ recorded by the ATLAS detector at the LHC is presented. The measured effective lifetime is $$ \tau = 1.5053 \pm 0.0012 ~\mathrm{(stat.)} \pm 0.0035 ~\mathrm{(syst.)~ps}. $$ The average deca

Carlos Misael Madrid Padilla, Zhi Zhang, Xiaokai Luo, Daren Wang

In this paper, we focus on fully connected deep neural networks utilizing the Rectified Linear Unit (ReLU) activation function for nonparametric estimation. We derive non-asymptotic bounds that lead to convergence rates, addressing both temporal and spatial dependence in the observed measurements. By accounting for dependencies across time and space, our mod

Jiaxing He, Bingzhe Hou, Tieru Wu, Yang Cao

Classification in the sense of similarity is an important issue. In this paper, we study similarity classification in Topological Data Analysis. We define a pseudometric $d_{S}^{(p)}$ to measure the distance between barcodes generated by persistent homology groups of topological spaces, and we provide that our pseudometric $d_{S}^{(2)}$ is a similarity invar

Sneihil Gopal, Aziz Kord, Richard A. Rouil

The emergence of 6G technology represents a significant advancement in wireless communications, providing unprecedented speed, extremely low latency, and pioneering applications. In light of this development, an important question arises: Can the Open Radio Access Network (O-RAN), with its emphasis on openness, flexibility, RAN slicing, RAN Intelligent Contr

Jin-Lu Wen, Jia-Dong Tang, Ya-Nan Lv, Yu R. Sun

Post-selecting output states in measurements can effectively amplify weak signals and improve precision. However, post-selection effects may also introduce unintended biases in precision measurements. Here, we investigate the influence of post-selection in the precision spectroscopy of the $2^3S - 2^3P$ transition of helium ($^4$He) using an atomic beam. We

Takumi Araya, Yuichi Sudo

This paper addresses the collision detection problem in population protocols. The network consists of state machines called agents. At each time step, exactly one pair of agents is chosen uniformly at random to have an interaction, changing the states of the two agents. The collision detection problem involves each agent starting with an input integer betwee

Xingzhou Chen, Nachuan Yang, Peihu Duan, Shilei Li

The implementation of cyber-physical systems in real-world applications is challenged by safety requirements in the presence of sensor threats. Most cyber-physical systems, especially multi-sensor systems, struggle to detect sensor attacks when the attack model is unknown. In this paper, we tackle this issue by proposing a Gaussian-Bernoulli Secure (GBS) est

Thanh Tam Nguyen, Zhao Ren, Trinh Pham, Thanh Trung Huynh

The rapid advancement of large language models (LLMs) and multimodal learning has transformed digital content creation and manipulation. Traditional visual editing tools require significant expertise, limiting accessibility. Recent strides in instruction-based editing have enabled intuitive interaction with visual content, using natural language as a bridge

Liwei Yuan, Hideaki Ishii

We study resilient leader-follower consensus of multi-agent systems (MASs) in the presence of adversarial agents, where agents' communication is modeled by time-varying topologies. The objective is to develop distributed algorithms for the nonfaulty/normal followers to track an arbitrary reference value propagated by a set of leaders while they are in intera

Qianhao Wang, Yinqian Sun, Enmeng Lu, Qian Zhang

Spiking Neural Networks (SNNs) has the ability to extract spatio-temporal features due to their spiking sequence. While previous research has primarily foucus on the classification of image and reinforcement learning. In our paper, we put forward novel diffusion policy model based on Spiking Transformer Neural Networks and Denoising Diffusion Probabilistic M

Jingxuan Chen

Avatar modelling has broad applications in human animation and virtual try-ons. Recent advancements in this field have focused on high-quality and comprehensive human reconstruction but often overlook the separation of clothing from the body. To bridge this gap, this paper introduces GGAvatar (Garment-separated 3D Gaussian Splatting Avatar), which relies on

Jia-Rui Lin, Zhen-Zhong Hu, Jian-Ping Zhang, Fang-Qiang Yu

As the information from diverse disciplines continues to integrate during the whole life cycle of an Architecture, Engineering, and Construction (AEC) project, the BIM (Building Information Model/Modeling) becomes increasingly large. This condition will cause users difficulty in acquiring the information they truly desire on a mobile device with limited spac

Elies Harington, Samuel Mimram

Polynomials in a category have been studied as a generalization of the traditional notion in mathematics. Their construction has recently been extended to higher groupoids, as formalized in homotopy type theory, by Finster, Mimram, Lucas and Seiller, thus resulting in a cartesian closed bicategory. We refine and extend their work in multiple directions. We b

Peng Chen, Lihu Xu, Xiaolong Zhang, Xicheng Zhang

By establishing the regularity estimates for nonlocal Stein/Poisson equations under $\gamma$-order H\"older and dissipative conditions on the coefficients, we derive the $W_{\bf d}$-convergence rate for the Euler-Maruyama schemes applied to the invariant measure of SDEs driven by multiplicative $\alpha$-stable noises with $\alpha \in (\frac{1}{2}, 2)$, where

Bhupendra Chauhan, Tapobroto Bhanja

We discuss and derive the off-shell nilpotent of order two and absolutely anti-commuting Becchi-Rouet-Stora-Tyutin (BRST), anti-BRST, co-BRST and anti-co-BRST symmetry transformations for the non-interacting Friedberg-Lee-Pang-Ren (FLPR) model in one (0 + 1)-dimension (1D) of spacetime by exploiting the standard techniques of the (anti-)chiral supervariable

Yahe Yang, Chunliang Tao, Xiaojing Fan

Effective preference tuning is pivotal in aligning chatbot responses with human expectations, enhancing user satisfaction and engagement. Traditional approaches, notably Reinforcement Learning from Human Feedback (RLHF) as employed in advanced models like GPT-4, have demonstrated considerable success in this domain. However, RLHF methods are often computatio

Zihui Ma, Guangxiao Hu, Ting-Syuan Lin, Lingyao Li

The increasing frequency and severity of wildfires pose significant risks to communities, infrastructure, and the environment, especially in Wildland-Urban Interface (WUI) areas. Effective disaster management requires understanding how the public perceives and responds to wildfire threats in real-time. This study uses social media data to assess public respo

Ding Li, Ziqi Zhang, Mengyu Yao, Yifeng Cai

Trusted Execution Environments (TEE) are used to safeguard on-device models. However, directly employing TEEs to secure the entire DNN model is challenging due to the limited computational speed. Utilizing GPU can accelerate DNN's computation speed but commercial widely-available GPUs usually lack security protection. To this end, scholars introduce TSDP, a

Thang M. Pham, Phat T. Nguyen, Seunghyun Yoon, Viet Dac Lai

While small language models (SLMs) show promises for mobile deployment, their real-world performance and applications on smartphones remains underexplored. We present SlimLM, a series of SLMs optimized for document assistance tasks on mobile devices. Through extensive experiments on a Samsung Galaxy S24, we identify the optimal trade-offs between model size

Songting Liu

Zero-shot voice conversion aims to transform a source speech utterance to match the timbre of a reference speech from an unseen speaker. Traditional approaches struggle with timbre leakage, insufficient timbre representation, and mismatches between training and inference tasks. We propose Seed-VC, a novel framework that addresses these issues by introducing

Masato Kobayashi, Thanpimon Buamanee, Takumi Kobayashi

Autonomous manipulation in everyday tasks requires flexible action generation to handle complex, diverse real-world environments, such as objects with varying hardness and softness. Imitation Learning (IL) enables robots to learn complex tasks from expert demonstrations. However, a lot of existing methods rely on position/unilateral control, leaving challeng

Serena Dipierro, Xifeng Su, Enrico Valdinoci, Jiwen Zhang

In this paper, we deal with the following mixed local/nonlocal Schr\"{o}dinger equation \begin{equation*} \left\{ \begin{array}{ll} - \Delta u + (-\Delta)^s u+u = u^p \quad \hbox{in $\mathbb{R}^n$,} u>0 \quad \hbox{in $\mathbb{R}^n$,} \lim\limits_{|x|\to+\infty}u(x)=0, \end{array} \right. \end{equation*} where $n\geqslant2$, $s\in (0,1)$ and $p\in\left(1,\fr

Tomohiro Ono, Tatsuki Okamura, Satoshi Okuzumi, Takayuki Muto

We investigate the heating of protoplanetary disks caused by shocks associated with spiral density waves induced by an embedded planet. Using two-dimensional hydrodynamical simulations, we explore the dependence of shock heating rates on various disk and planetary parameters. Our results show that the shock heating rates are primarily influenced by the plane

Alexander Mundey, Aidan Sims

We establish conditions under which an inclusion of finitely aligned left-cancellative small categories induces inclusions of twisted C*-algebras. We also present an example of an inclusion of finitely aligned left-cancellative monoids that does not induce a homomorphism even between (untwisted) Toeplitz algebras. We prove that the twisted C*-algebras of a j

Yiwei Tao, Miaowen Wen, Yao Ge, Jun Li

Affine frequency division multiplexing (AFDM) is a novel modulation technique based on chirp signals that has been recently proposed as an effective solution for highly reliable communications in high-mobility scenarios. In this paper, we focus on the design of robust index modulation (IM) schemes under the multiple-antenna AFDM transmission framework. To th

Masahiro Suzuki, Hiroki Sakaji

We aim to enhance a price sentiment index and to more precisely understand price trends from the perspective of not only consumers but also businesses. We extract comments related to prices from the Economy Watchers Survey conducted by the Cabinet Office of Japan and classify price trends using a large language model (LLM). We classify whether the survey sam

Xinyi Zhou, Danlan Huang, Zhixin Qi, Liang Zhang

Deep joint source-channel coding (DeepJSCC) has shown promise in wireless transmission of text, speech, and images within the realm of semantic communication. However, wireless video transmission presents greater challenges due to the difficulty of extracting and compactly representing both spatial and temporal features, as well as its significant bandwidth

Lei Shi, Xinghua Yu, Cheng Zhou, Wanxin Jin

This article propose a whole-body impedance coordinative control framework for a wheel-legged humanoid robot to achieve adaptability on complex terrains while maintaining robot upper body stability. The framework contains a bi-level control strategy. The outer level is a variable damping impedance controller, which optimizes the damping parameters to ensure

Satoshi Okuzumi

Planetesimal formation via the streaming and gravitational instabilities of dust in protoplanetary disks requires a local enhancement of the dust-to-gas mass ratio. Radial drift of large grains toward pressure bumps in gas disks is a plausible mechanism for achieving the required dust concentration. However, recent millimeter disk observations suggest that t

Kaito Baba, Ryota Yagi, Junichiro Takahashi, Risa Kishikawa

With the rapid advancement of large language models (LLMs), foundational models (FMs) have seen significant advancements. Healthcare is one of the most crucial application areas for these FMs, given the significant time and effort required for physicians to analyze large volumes of patient data. Recent efforts have focused on adapting multimodal FMs to the m

Shengpeng Ji, Yifu Chen, Minghui Fang, Jialong Zuo

Recent advancements in spoken dialogue models, exemplified by systems like GPT-4o, have captured significant attention in the speech domain. Compared to traditional three-tier cascaded spoken dialogue models that comprise speech recognition (ASR), large language models (LLMs), and text-to-speech (TTS), modern spoken dialogue models exhibit greater intelligen

Yafan Yang, Arun Kumar Narayanan Nair, Shuyu Sun, Denvid Lau

Fluid-solid interfacial free energy (IFE) is a fundamental parameter influencing wetting behaviors, which play a crucial role across a broad range of industrial applications. Obtaining reliable data for fluid-solid IFE remains challenging with experimental and semi-empirical methods, and the applicability of first-principle theoretical methods is constrained

Lan Huang, Xiaodong Yang

This paper studies the steady-state oscillations and the corresponding energy transfers in the two-DOF mechanical system with a grounded nonlinear energy sink (NES), under an external excitation. Based on the complexification-multiscale method, the slow frequency and the fast frequency of the complex amplitudes on the invariant manifold are analyzed, without

Xifeng Su, Enrico Valdinoci, Yuanhong Wei, Jiwen Zhang

This article is concerned with ``up to $C^{2, \alpha}$-regularity results'' about a mixed local-nonlocal nonlinear elliptic equation which is driven by the superposition of Laplacian and fractional Laplacian operators. First of all, an estimate on the $L^\infty$ norm of weak solutions is established for more general cases than the ones present in the literat

Chung Hee Kim, Abhisesh Silwal, George Kantor

Automating tasks in outdoor agricultural fields poses significant challenges due to environmental variability, unstructured terrain, and diverse crop characteristics. We present a robotic system for autonomous pepper harvesting designed to operate in these unprotected, complex settings. Utilizing a custom handheld shear-gripper, we collected 300 demonstratio

Qi Hao, Runchang Liang, Yue Gao, Hao Dong

Variable Subset Forecasting (VSF) refers to a unique scenario in multivariate time series forecasting, where available variables in the inference phase are only a subset of the variables in the training phase. VSF presents significant challenges as the entire time series may be missing, and neither inter- nor intra-variable correlations persist. Such conditi

A. Hammad, Mihoko M. Nojiri, Masahito Yamazaki

Anomaly detection is a vital technique for exploring signatures of new physics Beyond the Standard Model (BSM) at the Large Hadron Collider (LHC). The vast number of collisions generated by the LHC demands sophisticated deep learning techniques. Similarity learning, a self-supervised machine learning, detects anomalous signals by estimating their similarity

Shuai Huang, Thomas Denney, Deqiang Qiu

Motivation - The test-retest reliability of quantitative susceptibility mapping (QSM) is affected by parameters of the acquisition protocol such as the angulation of acquisition plane with respect to the B0 field direction and spatial resolution. Goal - We aim to reduce the protocol-dependent biases/errors that might overshadow subtle changes of the suscepti

Ajani De Vas Gunasekara, Daniel Horsley

A $k$-star is a complete bipartite graph $K_{1,k}$. A partial $k$-star design of order $n$ is a pair $(V,\mathcal{A})$ where $V$ is a set of $n$ vertices and $\mathcal{A}$ is a set of edge-disjoint $k$-stars whose vertex sets are subsets of $V$. If each edge of the complete graph with vertex set $V$ is in some star in $\mathcal{A}$, then $(V,\mathcal{A})$ is

Yuhao Gu, Jiangping Hu, Hong Jiang, Tao Xiang

Boron-rich lithium borocarbides are promising candidates for phonon-mediated high-temperature superconductors due to their metallic $\sigma$-bonding electrons. Here, we use the cluster expansion method to identify energetically stable configurations (colorings) of Li$_2$B$_3$C and Li$_3$B$_4$C$_2$, which are characterized by a distinctive pattern of alternat

Zhenjun Zhang, Lijun Tang, Hongjin Wang, Lilian Zhang

Computer vision is increasingly used in areas such as unmanned vehicles, surveillance systems and remote sensing. However, in foggy scenarios, image degradation leads to loss of target details, which seriously affects the accuracy and effectiveness of these vision tasks. Polarized light, due to the fact that its electromagnetic waves vibrate in a specific di

Yuanyuan Bao

In this note, we reformulate the invariant $\Delta (M, \omega)$ that we defined before, and show its relation with Reidemeister torsion. We calculate $\Delta (M, \omega)$ when the $3$-manifolds are lens spaces, and discuss the classification of the lens spaces using $\Delta (M, \omega)$.

Yavar Kian, Hongyu Liu, Li-Li Wang, Guang-Hui Zheng

In this article we study the inverse problem of determining a semilinear term appearing in an elliptic equation from boundary measurements. Our main objective is to develop flexible and general theoretical results that can be used for developing numerical reconstruction algorithm for this inverse problem. For this purpose, we develop a new method, based on d

Andong Deng, Tongjia Chen, Shoubin Yu, Taojiannan Yang

In this paper, we introduce Motion-Grounded Video Reasoning, a new motion understanding task that requires generating visual answers (video segmentation masks) according to the input question, and hence needs implicit spatiotemporal reasoning and grounding. This task extends existing spatiotemporal grounding work focusing on explicit action/motion grounding,

Cruz Godar, Benjamin Young

Pandharipande-Thomas theory and Donaldson-Thomas theory (PT and DT) are two branches of enumerative geometry in which particular generating functions arise that count plane-partition-like objects. That these generating functions differ only by a factor of MacMahon's function was proven recursively by Jenne, Webb, and Young using the double dimer model. We bi

Maiyuren Srikumar, Stephen D. Bartlett, Angela Karanjai

Contextuality is a key characteristic that separates quantum from classical phenomena and an important tool in understanding the potential advantage of quantum computation. However, when assessing the quantum resources available for quantum information processing, there is no formalism to determine whether a set of states can exhibit contextuality and whethe

A. Lauer-Coles, C. M. Deibel, J. C. Blackmon, A. Hood

Background: Type I X-Ray bursts (XRBs) are energetic stellar explosions that occur on the surface of a neutron star in an accreting binary system with a low-mass H/He-rich companion. The rate of the $^{34}$Ar($\alpha,p$)$^{37}$K reaction may influence features of the light curve that results from the underlying thermonuclear runaway, as shown in recent XRB s

Yan Jiang, Hongyu Liu, Tianhao Ni, Kai Zhang

We propose and study several inverse boundary problems associated with a quasilinear hyperbolic equation of the form ${c(x)^{-2}}\partial_t^2u=\Delta_g(u+F(x, u))+G(x, u)$ on a compact Riemannian manifold $(M, g)$ with boundary. We show that if $F(x, u)$ is monomial and $G(x, u)$ is analytic in $u$, then $F, G$ and $c$ as well as the associated initial data

Jiessie Tie, Bingsheng Yao, Tianshi Li, Hongbo Fang

Software engineers are increasingly incorporating AI assistants into their workflows to enhance productivity and alleviate cognitive load. However, experiences with large language models (LLMs) such as ChatGPT vary widely. While some engineers find them useful, others deem them counterproductive due to inaccuracies in their responses. Researchers have also o

Luoyu Mei, Ruofeng Liu, Zhimeng Yin, Qingchuan Zhao

Virtual reality (VR), while enhancing user experiences, introduces significant privacy risks. This paper reveals a novel vulnerability in VR systems that allows attackers to capture VR privacy through obstacles utilizing millimeter-wave (mmWave) signals without physical intrusion and virtual connection with the VR devices. We propose mmSpyVR, a novel attack

Lakshmikanta Sau, Priyadarshi Mukherjee, Sasthi C. Ghosh

This paper studies a graph-based sensor deployment approach in wireless sensor networks (WSNs). Specifically, in today's world, where sensors are everywhere, detecting various attributes like temperature and movement, their deteriorating lifetime is indeed a very concerning issue. In many scenarios, these sensors are placed in extremely remote areas, where m

J. Vandeleur, G. Sanamyan, B. M. Roberts, J. S. M. Ginges

The Moskowitz-Lombardi rule gives a simple relation between the magnetic moment of an atomic nucleus and the effect of its radial distribution on the hyperfine structure - the magnetic hyperfine anomaly or "Bohr-Weisskopf" effect. It was originally formulated for mercury, for which experimental data for nuclear magnetic moments and hyperfine constants were a